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Tests May Reveal Hidden Predictors of Heart Disease

EKG patterns show who will likely die after a heart attack.

A new approach to analyzing electrocardiograms–a ubiquitous test of the heart’s electrical function–could predict who is most likely to die after a heart attack. Researchers at MIT found that measuring how much the shape of the electrical waveform varies from beat to beat identifies high-risk patients better than existing risk factors. If the findings hold up in further clinical trials, the technology could be used to figure out which heart attack patients need the most aggressive treatment.

Shape shifting: Researchers at MIT use a technique called dynamic time warping to compare the shape of individual waves in an electrocardiogram, shown here. This approach aligns waves based on features that correspond to the same underlying physiological activity, allowing an automated and accurate comparison. The measure, dubbed morphological variability, appears to predict who is at greatest risk of death after a heart attack.

Scientists hope the same approach will eventually help them predict when a healthy person is likely to suffer a cardiac problem. They are working with Texas Instruments to integrate the software into the new generation of wearable heart monitors.

The research also shows how computational analysis can glean useful information from the reams of medical data routinely collected and ignored. “It’s a very novel approach,” says Jean-Philippe Couderc, biomedical engineer at the University of Rochester, who was not involved in the project. “It’s a unique way of looking at how the electrocardiogram varies on a beat to beat basis.”

Electrocardiograms record the heart’s electrical activity through sensors placed on the chest. Cardiologists can then spot abnormal heart rhythms by visually inspecting the resulting waveform for major features linked to the function of the top and bottom chambers of the heart, as well as the heart’s ability to “reset” itself between beats. While some simple algorithms exist to analyze this data, they are notoriously inaccurate. “Cardiologists routinely ignore them,” says Collin Stultz, a professor at MIT, as well as a practicing cardiologist, who is involved in the project.

To determine if more subtle features within electrocardiogram data could provide useful clinical information, Stultz, John Guttag, also at MIT, and Zeeshan Syed, now at the University of Michigan, started with a large data set of 24-hour electrocardiogram recordings collected at Brigham and Women’s Hospital in Boston as part of a clinical trial for a new drug. Employing a number of computational techniques, including signal processing, data mining, and machine learning, the researchers developed a way to analyze how the shape of the electrical waveform varies, a measure they dubbed morphological variability. At the heart of the approach is a method called dynamic time warping, used in speech recognition and more recently in genome analysis, which allows researchers to align and compare individual beats. “We compute the differences for every pair of beats,” says Stultz. “If there is lots of variability, that patient is in bad shape.”

The team then applied the algorithm they had developed to a second set of electrocardiogram recordings and found that patients with the highest morphological variability were six to eight times more likely to die after a heart attack than those with low variability. “We found that it consistently works as well or better than commonly accepted risk metrics that physicians use,” says Stultz, including diabetes, age and smoking status, as well as cardiac ultrasound and various blood tests.

Researchers are now planning a number of new clinical studies, including assessing morphological variability in healthy people to get a sense of how this measure varies among a normal population. (The existing research was done on recordings from patients with a history of heart disease.) In addition, the team is modifying the algorithm to shorten the amount of data needed to make the predictions from about 10 hours’ worth of EKG data to less than one. “We hope to get it down to half an hour, which is within the realm of a doctor’s visit,” says Guttag.

They also want to determine whether measuring morphological variability in patients at high risk of developing cardiovascular disease can predict risk of heart attack or death in those who have not yet had a cardiac event.

“It’s an extra test, but it’s one that is cheap, because the data is already available and the analysis can be performed on existing computers,” says Guttag. But the technology still faces a number of hurdles. A prospective trial is needed to confirm that morphological variability is a clinically useful marker. And researchers need to figure out how cardiologists can easily access and analyze the information–the different devices currently used to record EKGs typically lock the data behind protective software.

In the longer term, the researchers aim to incorporate this type of analysis into a new generation of heart monitors currently under development. These monitors are intended to be so small, cheap, and easy to use that people wear them all the time–similar to the heart-rate monitors athletes often wear but able to collect more sophisticated information.

The team is collaborating with Texas Instruments to develop a wearable monitor with an embedded chip to calculate morphological variability in real time. Such a device might be used to signal a patient to take additional medication or signal an implanted device to deliver a jolt of electricity to the heart.

Cardiologists themselves may prove to be a serious obstacle in bringing this type of analysis to clinical practice. Not only is morphological variability too subtle a measure to detect visually, it is not yet clear what it represents physically in the heart. “It’s going to be difficult for a nonengineer to link this measurement to specific abnormal electrophysiological phenomena,” says Couderc. “That needs to be clarified and communicated to the medical field.”

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